# Git Fundamentals (Data Science & ML)

> Commits, branches, merge, rebase, conflicts, pull requests, GitHub, collaborative workflows

- 18 interview questions
- Junior
- [Interview Questions: Data Science & ML](https://sharpskill.dev/en/technologies/data-science/interview-questions.md)

## 1. What is Git?

**Answer**

Git is a distributed version control system created by Linus Torvalds in 2005. Unlike centralized systems like SVN, each developer has a complete copy of the project history locally. This allows working offline and offers great flexibility for collaborative workflows.

## 2. Which command initializes a new Git repository in a directory?

**Answer**

The git init command creates a new Git repository in the current directory. It generates a hidden .git folder containing all the structure needed for version tracking: commit history, branches, local configuration and Git objects.

## 3. What is the difference between git add and git commit?

**Answer**

Git uses an intermediate staging area (index) between modified files and history. git add moves changes to this staging area, preparing files for the next commit. git commit then records the staging area content into the permanent repository history.

## 15 more questions available

- What is a commit in Git?
- Which command shows the current state of the Git repository?

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## Other Data Science & ML interview topics

- [Python Basics](https://sharpskill.dev/en/technologies/data-science/interview-questions/python-basics.md): 25 questions, Junior
- [Python Object-Oriented Programming](https://sharpskill.dev/en/technologies/data-science/interview-questions/python-oop.md): 20 questions, Junior
- [Python Data Structures](https://sharpskill.dev/en/technologies/data-science/interview-questions/python-data-structures.md): 20 questions, Junior
- [SQL Basics](https://sharpskill.dev/en/technologies/data-science/interview-questions/sql-basics.md): 20 questions, Junior
- [NumPy Fundamentals](https://sharpskill.dev/en/technologies/data-science/interview-questions/numpy-fundamentals.md): 22 questions, Junior
- [Pandas Basics](https://sharpskill.dev/en/technologies/data-science/interview-questions/pandas-basics.md): 22 questions, Junior
- [Jupyter & Google Colab](https://sharpskill.dev/en/technologies/data-science/interview-questions/jupyter-colab.md): 16 questions, Junior
- [SQL Joins & Advanced Queries](https://sharpskill.dev/en/technologies/data-science/interview-questions/sql-joins-advanced.md): 22 questions, Mid-Level
- [Advanced Pandas](https://sharpskill.dev/en/technologies/data-science/interview-questions/pandas-advanced.md): 24 questions, Mid-Level
- [Visualization with Matplotlib & Seaborn](https://sharpskill.dev/en/technologies/data-science/interview-questions/matplotlib-seaborn.md): 20 questions, Mid-Level
- [Interactive Visualizations with Plotly](https://sharpskill.dev/en/technologies/data-science/interview-questions/plotly-interactive.md): 18 questions, Mid-Level
- [Descriptive Statistics](https://sharpskill.dev/en/technologies/data-science/interview-questions/statistics-descriptive.md): 20 questions, Mid-Level
- [Inferential Statistics](https://sharpskill.dev/en/technologies/data-science/interview-questions/statistics-inferential.md): 24 questions, Mid-Level
- [Web Scraping](https://sharpskill.dev/en/technologies/data-science/interview-questions/web-scraping.md): 18 questions, Mid-Level
- [BigQuery & Cloud Data](https://sharpskill.dev/en/technologies/data-science/interview-questions/bigquery-cloud.md): 18 questions, Mid-Level
- [Feature Engineering](https://sharpskill.dev/en/technologies/data-science/interview-questions/feature-engineering.md): 22 questions, Mid-Level
- [Supervised ML: Regression](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-supervised-regression.md): 24 questions, Mid-Level
- [Supervised ML: Classification](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-supervised-classification.md): 24 questions, Mid-Level
- [Decision Trees & Ensembles](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-trees-ensembles.md): 24 questions, Mid-Level
- [Unsupervised ML](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-unsupervised.md): 22 questions, Mid-Level
- [ML Pipelines & Validation](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-pipelines-validation.md): 22 questions, Mid-Level
- [Time Series & Forecasting](https://sharpskill.dev/en/technologies/data-science/interview-questions/time-series-forecasting.md): 22 questions, Mid-Level
- [Deep Learning Fundamentals](https://sharpskill.dev/en/technologies/data-science/interview-questions/deep-learning-fundamentals.md): 24 questions, Senior
- [TensorFlow & Keras](https://sharpskill.dev/en/technologies/data-science/interview-questions/tensorflow-keras.md): 22 questions, Senior
- [CNN & Image Classification](https://sharpskill.dev/en/technologies/data-science/interview-questions/cnn-image-classification.md): 24 questions, Senior
- [RNN & Sequences](https://sharpskill.dev/en/technologies/data-science/interview-questions/rnn-sequences.md): 22 questions, Senior
- [Transformers & Attention](https://sharpskill.dev/en/technologies/data-science/interview-questions/transformers-attention.md): 24 questions, Senior
- [NLP & Hugging Face](https://sharpskill.dev/en/technologies/data-science/interview-questions/nlp-huggingface.md): 24 questions, Senior
- [GenAI & LangChain](https://sharpskill.dev/en/technologies/data-science/interview-questions/genai-langchain.md): 24 questions, Senior
- [MLOps & Deployment](https://sharpskill.dev/en/technologies/data-science/interview-questions/mlops-deployment.md): 24 questions, Senior

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